The present invention relates generally to image systems and, more particularly, to systems and methods for segmenting an organ in a plurality of images.
During clinical diagnosis, a patient's internal anatomy is imaged to determine how a disease has progressed. An infected tissue (such as tumor mass) shows some differences from a normal tissue. Also, the patient may have some type of individual differences or abnormalities regarding even healthy tissues.
Several modalities are used to generate images of the patient's internal anatomy or functionality, suitable for diagnostic purposes, radiotherapy treatment, or for surgical planning. Exemplary modalities include conventional X-ray plane film radiography; computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”); and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”).
In a case of radiation treatment (“RT”) planning, CT imaging is generally used because an image pixel gray value (Hounsfield Units) is a direct function of a radiation dose calculation. A CT image is three dimensional (3D), more precisely, the CT image is a collection of adjacent transaxial two dimensional (2D) slices. Clinicians undertake a process of recombining anatomical elements of 2D slices to form a 3D object or an organ to get anatomical data about the patient being treated. The process of recombining anatomical elements as stated above is usually termed a reconstruction.
RT planning typically involves, clinicians such as, for example, radiologists, dosimetrists or radiotherapists, tracing outlines of a few critical structures on a number of image slices. Manually tracing the outlines on a contiguous set of 2D slices and then combining them can be time consuming and labor intensive. Time and labor increase significantly both as the number of image slices increase, and as a number and size of an organ, tumor, etc. in an anatomical area of interest increases. Quality of the outlining and quality of a produced 3D object depend on a resolution and contrast of the 2D slices, and on knowledge and judgment of the clinician performing the reconstruction.
Using an automated image segmentation could save time and labor that would otherwise be needed if using manual tracing. Also, automated image segmentation could increase precision (intra-operator repeatability and inter-operator reproducibility) by eliminating subjectivity of the clinician.
Automated image segmentation of organs in the abdominal region faces certain challenges. Abdominal organs such as, for example, a spleen, a kidney, and a liver, are located in a soft tissue environment wherein while parts of their boundary have good contrast resolution against surrounding structures others have poor contrast. For example, in most cases, the left kidney is touching the spleen in some slices and the right kidney is touching the liver. An intensity difference between each organ is very small. A very small intensity difference causes separation of each organ, if “touching” slice images are contained within the region of interest (ROI), to be very difficult and, in some cases, impossible. Additionally, between acquisitions of the slice images, abdominal organs are displaced by respiratory motion, resulting in significantly jagged outlines in planes tilted relative to a plane of the slice image.
Characteristics of abdominal organs also change from patient to patient including for example, shape, size and location of the organ. Imaging parameters of CT machines vary as well. Thus, it is desirable to obtain a method to obtain automatically segmented image data of abdominal organs.